Spaces:
Sleeping
Sleeping
File size: 2,496 Bytes
c372ed2 340ce5a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | name: sieve
version: "1.0.0"
description: >
A customer support reinforcement learning environment where an agent triages,
classifies, drafts responses to, and prioritizes real-world support emails.
tags:
- openenv
- customer-support
- email-triage
- nlp
api:
base_url: http://localhost:7860
reset: POST /reset
step: POST /step
state: GET /state
tasks: GET /tasks
tasks:
- id: email_classification
name: Email Classification
difficulty: easy
description: >
Classify each incoming customer support email by category
(billing/technical/general/spam/account/feature_request)
and urgency (high/medium/low) using action_type='classify'.
max_steps: 15
score_range: [0.0, 1.0]
- id: response_drafting
name: Response Drafting
difficulty: medium
description: >
Draft professional, empathetic responses to customer support emails.
Each response must cover required keywords, exceed 50 characters,
and maintain a professional tone. Use action_type='respond'.
max_steps: 10
score_range: [0.0, 1.0]
- id: support_session
name: Full Support Session
difficulty: hard
description: >
Manage a queue of 15 mixed emails. Prioritize VIP customers first,
then high-urgency emails. Choose the correct action (respond/escalate/archive)
per email, provide category and urgency, and use email_id to select
which email to process each step.
max_steps: 40
score_range: [0.0, 1.0]
observation_space:
current_email:
id: string
subject: string
body: string
sender: string
sender_tier: "standard | vip"
received_minutes_ago: integer
email_queue: "array of Email objects (populated in support_session only)"
processed_count: integer
step_count: integer
task_id: string
task_description: string
available_actions: "array of strings"
context:
max_steps: integer
remaining_steps: integer
queue_size: integer
action_space:
action_type: "classify | respond | escalate | archive | skip"
category: "billing | technical | general | spam | account | feature_request"
urgency: "high | medium | low"
response_text: "string — required for respond"
escalation_reason: "string — required for escalate"
email_id: "string — used in support_session to select target email"
baseline:
agent: gpt-4o-mini
scores:
email_classification: 0.930
response_drafting: 0.920
support_session: 0.882
average: 0.911
|